蒙特卡罗方法 Resources

Showing items tagged with "蒙特卡罗方法"

Application Background: Quantitative calculation of top event probability is a crucial aspect of fault tree analysis. Traditional formula-based approaches often involve heavy computational loads, complex procedures, and high error susceptibility. This project implements Monte Carlo simulation for fault tree models to accurately compute top event probabilities and other reliability metrics. Practical examples demonstrate this method's simplicity, high precision, and significant value for complex system reliability analysis. Key Technologies: Based on digital relay protection system functionality and operational characteristics, this research proposes a quantitative analysis method for dynamic reliability assessment. The approach helps identify system vulnerabilities and improve protection design/operation reliability through established metrics including cumulative failure probability, availability, and component importance measures.

MATLAB 432 views Tagged

This resource covers the core principles and applications of Monte Carlo methods, featuring numerous MATLAB implementation examples with detailed algorithm explanations. It serves as comprehensive learning material for understanding both theoretical foundations and practical coding techniques, requiring no extraction password.

MATLAB 291 views Tagged

Monte Carlo method, also known as statistical simulation method or random sampling technique, is a stochastic simulation approach based on probability and statistical theory. It employs random numbers (or more commonly pseudo-random numbers) to solve various computational problems. This method connects the target problem with a specific probability model and uses computer statistical simulation or sampling to obtain approximate solutions. Key implementation aspects include random number generation using functions like rand() or randn(), probability distribution modeling, and iterative sampling processes.

MATLAB 229 views Tagged